Understanding Customer Behavior Through Data Analytics
Most people think data analytics is somewhat a genie in a bottle. They expect it to spit out precise answers about what customers want, when, and how they’ll behave. The reality is - and I hate to be the bearer of bad news - it’s not quite that simple.
Customer behaviour is nuanced, influenced by external factors like culture and the economy, and personal ones like mood. In my experience with retail analytics, there’s often a surprise around what insights we get from customer data. Sometimes the information confirms what we already know about our target audience.
It seems like but there are times when things don’t add up, which is always interesting to explore. For instance, you could have loyal customers who have been coming in for years suddenly stop buying a product they’ve always loved. And that’s the thing about data analytics - you need context to understand it.
It’s simply not enough to look at numbers on a spreadsheet or graphs of historical sales cycles. More or less. You have to bring your expertise as a retailer into play and piece together what the missing link could be based on external factors like lifestyle trends or the weather.
Sort of. And that can feel scary because it means you don’t always know what’s happening. But customers are unpredictable at times and their behaviour can change as their lifestyles do. You can’t always anticipate that but you can try to understand it through data analytics.
This helps you paint a more accurate picture of who your audience is and what their needs are today so you can forecast what they might want tomorrow - even if it feels completely out of left field sometimes.
Leveraging Predictive Analytics for Inventory Management
Looks Like what i’ve seen plenty of retailers do, is they get a bit too obsessed with the numbers and believe that predictive analytics can solve everything. Like you can go from making random guesses to being the Oracle of Delphi or something overnight. And that’s not how this works. Yes, predictive analytics is great for inventory management, but you need to have some fairly sound knowledge about what your customers want and why in the first place.
Data isn’t all one thing. There’s a lot to consider when it comes to leveraging predictive analytics - as it happens, the only time I’ve said leverage and not immediately cringed because of how much sense it makes - and that means knowing what’s important for your store and where you’re willing to make compromises if necessary. There’s no point giving up a heap of shelf space for a trending item on TikTok if your customer base hates it.
It’s something that’s happened before in far more stores than people would like to admit. I think there’s also a fair amount of trial and error involved with how data fits into your store decisions - at least initially. I mean, not so much now as was the case before when things were fairly new to most people, but even now, there’s still an element of error involved. The main thing is to keep an eye on how things move and how customers respond because there are certain times when you’ll realise that you need to ask better questions about what the data is telling you.
You need to bring nuance back into these systems so you can slightly focus on building relationships with customers while automating some tasks. We’re looking at repeat purchases, abandoned cart reminders, return rates - all sorts of stuff that help us learn more about our customers so we can serve them better - using information we’ve gained from using predictive analytics right alongside our gut feelings about product movement based on years of experience. It feels like working smarter instead of harder just happens naturally at this point - even though we know how much work goes into setting things up in the first place.
Enhancing In-Store Experience with Real-Time Data
A lot of store managers want a perfect answer before they do anything. That's the worst possible way to use real time data. The right approach is to start using data as you go.
We don't have months to decode the wave patterns behind why mothers, kids, teenagers, and so on shop differently. What we do need to do is look at what they're buying, when they're buying it, and what else has their attention while they're in the store. That's what sets apart a future proof store from one that's relying on century old ideas of how people like shopping. Shopping isn't meant to be fun anymore.
It's just another thing you need to do. Buy food, get accessories, find what you need for uni - it's not an experience in itself anymore. People want to be entertained or comforted while they're shopping for things they need.
How well are physical stores leveraging that. Well, not all that well yet. That's where real time customer data comes in. More or less.
We already talked about customers being more invested in their phones than the shopping experience. Data shows that customers want things to be easier and the last thing they want is a bit what they refer to as 'friction' (long queues). One way to fix this is occasionally to have more checkouts but that's an unnecessary cost when you can have four checkout counters with smart self-checkouts that automatically process products.
That's just one of the many ways retailers are using real time data these days. There's absolutely no need for complexity when looking at real time data. Onboarding a new system does involve some upskilling or reskilling but once that's done, customer movement tracking and heat maps can apparently help physical stores change up their product placements far more frequently (and profitably) than would otherwise be possible.
Utilizing Geographic Information Systems for Store Placement
Many believe that location is simply about finding a spot with the highest foot traffic or parking space. Implies That you know, that classic statement - ‘it’s all about location, location, location’. While that may be true in some cases, it might not necessarily be true for every retailer.
The best location for your store varies depending on the kind of product you sell and your target audience’s preferences. The rise of e-commerce has also made things slightly more challenging when it comes to understanding ideal locations. I often see brands overlook data as an important factor in choosing a store’s location. With data-driven retail offering more and more nuanced insights, retailers are better poised to understand customers, markets and locations.
More or less. Geographic Information Systems (GIS) can help inform smarter decisions around store placement. Sort of.
GIS-based solutions can offer retailers granular intelligence that combine sales data and market analysis with topography to develop an optimised site selection model. One of the main ways GIS can help is by narrowing down high-performing areas based on consumer demand and demographics. It has the potential to forecast trends in customer patterns based on behaviour and geography to better determine if a current store needs improvements or if a new one is required at all.
For example, a furniture brand could use this technique to identify populations in a specific suburb who are searching for luxury wooden flooring as they consider opening a new store specialising in this product alone. I think there are still some hesitations when it comes to leveraging GIS because of its complexity but things should get easier over time. Some believe it can be expensive and others believe it is too advanced for their business needs.
But once you think through your current approach to figuring out where your customer base is for each product, you’ll get there slowly but steadily.
Integrating Omnichannel Data for Cohesive Decision-Making
Most teams believe that any old omnichannel reporting system will do. But it isn’t about simply gathering masses of data. The skill lies in making sense of it all and the truth is that few are able to do this effectively. Most enterprises have invested heavily in disparate and isolated systems leading to a lack of cross-channel visibility, which leads to poor forecasting, inventory issues, and declining customer experience.
To avoid this, several retailers are now integrating store data with all other channels across the value chain for a holistic view of the business, enabling teams to take better decisions with higher accuracy. At its core, this is about combining data from all channels and sources into one platform, providing users access to real-time information about anything they need - whether it’s sales, inventory, supply chain updates or more. This empowers teams with intelligence on how different channels impact each other and what areas have further potential.
For instance, many traditional retailers use store data for inventory and assortment purposes but miss out on leveraging digital campaigns to drive store sales and vice versa. By integrating omnichannel data into a single source of intelligence accessible by all users across the organisation, the enterprise can presumably forecast demand more accurately for every channel and product category; optimise planning; ensure pricing consistency; offer great customer experiences; and ultimately build loyalty through seamless shopping journeys regardless of how or where the transaction happens. In the age of personalisation, customers expect retailers to get things right every time.
And omnichannel data integration makes this possible by revealing previously hidden connections between digital touchpoints like mobile apps, social media engagement, website behaviour etc. Sort of. And their relationship with in-store shopping patterns.
When done right, it removes silos between departments so everyone from operations to marketing to finance is able to make more informed decisions. With advancements in technology platforms enabling easier integrations and intelligent solutions that can analyse large volumes of data on the fly, teams can now spend less time worrying about cleaning up information and more time taking action on insights that can move the needle faster.
Measuring Success: Key Performance Indicators for Retail Data Strategies
It’s not rare to see retailers placing their hopes on a single ‘magic’ metric when it comes to measuring data success. There’s an expectation that everything will be clear and sorted if they can pretty much pin their faith on one all-important KPI. In reality, there are several benchmarks needed to provide an accurate indication of what’s working and what isn’t.
The issue is that using KPIs as a tool of measurement requires more than finding a number you like and then tracking it consistently. Most metrics have to be interpreted in context, meaning you’ll need to keep an eye on other data points too. When a retailer tracks just one KPI, there’s always the risk that they miss out on something important or don’t consider how the metric is influenced by other factors - positive or negative.
This is where there can be some complexity, especially if you aren’t sure about what KPIs make sense for your business model. You’ll need to look at both short-term (think conversion rates) and long-term (think customer loyalty) metrics so that you have a comprehensive view of how your retail strategy is evidently performing. Not everything needs to be measured though. But KPIs do offer valuable insight into what’s working for your retail business.
When monitored regularly, these performance indicators help ensure that decision-making is rooted in real-time data instead of guesswork.